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INTEL 2021
Object Size Detection with the
OpenVINO™ Toolkit
Case study
Using OpenVINO with Windows on the Intel®
Neural Compute Stick 2 - Engineering Bench Talk
▲
Traditional methods of defect detection faced a
number of challenges that reduced the quality of the
process. Applying deep learning algorithms to captured
video information increases the speed and accuracy
of identifying objects that do not meet a predefined
standard. Though deep learning is a relatively new
solution for defect detection, it can expand the scope
of the solution from simple detection of a defect to
classification of the type of defect. Training deep learning
networks to identify types of defects makes it possible to
automatically route objects based upon their severity—
such as the size of the flaw. In this example of the Intel®
OpenVINO™ toolkit, we will look at a simple example of
how video images can be used to determine whether an
object is defective based upon its surface area.
Object Size Detection Pipeline
In prior blog posts, we've seen examples of face and vehicle
detection using images captured by a video camera. In this
application, we'll look at a different type of detection using
deep learning to identify an object on a conveyor belt,
measure its surface area, and check for defects.
M. Tim Jones, Mouser Electronics
Explore how you can use the
Intel
®
OpenVINO
™
toolkit to
automatically identify and
classify defective objects on a
conveyor belt.
